Digital image editing refers generally to the use of computer software and associated hardware to access a digital image file and perform modifications on the image of the digital image file. In particular, it may be desirable to perform colorization of such an image. That is, for example, one aspect of the technical field of digital image editing includes editing an image to change a color of an object in that image.
In practice, digital images may be constructed, stored, and rendered on a screen, using different techniques. For example, a raster image generally defines a digital image on a pixel-by-pixel basis. In another example, a vector image generally defines a digital image in terms of geometric shapes.
Existing colorization techniques include the use of machine learning, e.g., neural networks, to colorize a raster image. Raster images, however, may have varying levels of resolution or other measures of quality. For example, a raster image may have a given pixel density, e.g., dpi (dots per inch). In general, a raster image may not be scalable to a desired extent. For example, a raster image with a given number of pixels may appear suitable at a first size, but may appear pixilated or otherwise distorted if scaled up to a second, larger size.
On the other hand, vector images are generally scalable to a desired size. For example, a vector image can be scaled by mathematically increasing each of the included geometric shapes proportionally, and then rendering the result.
As referenced above, existing colorization techniques are optimized for raster images. As a result, colorized vector images may be low-quality, and/or may be laborious and time-consuming for users to construct (e.g., may be constructed manually). Thus, even to the extent that automated colorization may be available, the existing techniques produce low-quality results, and require excessive computing resources and/or time to reach a desired result.